Xiaoyu is a Postdoctoral Research Fellow in the Department of Mathematics and Statistics at the University of Exeter. Her research interests focus on using statistical and machine learning models as a tool for solving problems in the real world, such as making better decisions under uncertainty or answering questions about processes we have data for, whilst taking account of the accuracy, efficiency and scalability of the models. She specialises in Gaussian processes (GPs) modelling, uncertainty quantification (UQ) and their applications.

Xiaoyu currently works in a project investigating UQ methods for propagating and quantifying uncertainty in hierarchies of numerical codes. Between Feb 2021 and Oct 2022, she worked in the ‘Uncertainty Quantification for Expensive COVID-19 Simulation Models’ project, where she used GP emulator and history matching to calibrate a high-resolution spatial Covid-19 simulation model in real-time to enable fast high-resolution forecasts of Covid-19 spread with accurate uncertainty in real-time and under policy interventions. Between Dec 2017 and Jan 2021, she worked in the project ‘Big data methods for improving windstorm footprint prediction (BigFoot)’, where she developed data blending frameworks based on GPs for improved wind gust speed prediction accuracy.

Xiaoyu received her PhD in Computing Science (Machine Learning) in 2017 from the University of Glasgow. The topic of her PhD was Adaptive Multiple Importance Sampling for Gaussian processes.

https://orcid.org/0000-0003-3567-8965